{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "40f2658c-2def-430b-a6d4-1c3baa29609a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 4us/step \n",
      "x_train.shape= (60000, 28, 28)\n",
      "y_train.shape= (60000,)\n",
      "x_test.shape= (10000, 28, 28)\n",
      "y_test.shape= (10000,)\n"
     ]
    }
   ],
   "source": [
    "#p112\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "mnist=tf.keras.datasets.mnist\n",
    "(x_train,y_train),(x_test,y_test)=mnist.load_data()\n",
    "x_train,x_test=tf.cast(x_train,dtype=tf.float32)/255.0,tf.cast(x_test,dtype=tf.float32)/255.0\n",
    "y_train,y_test=tf.cast(y_train,dtype=tf.int32),tf.cast(y_test,dtype=tf.int32)\n",
    "print('x_train.shape=',x_train.shape)\n",
    "print('y_train.shape=',y_train.shape)\n",
    "print('x_test.shape=',x_test.shape)\n",
    "print('y_test.shape=',y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "affaece5-742b-43fa-b4e5-a750e70d8d78",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25088</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">3,211,392</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │           \u001b[38;5;34m160\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m4,640\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25088\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │     \u001b[38;5;34m3,211,392\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,217,482</span> (12.27 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,217,482\u001b[0m (12.27 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,217,482</span> (12.27 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,217,482\u001b[0m (12.27 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#p114\n",
    "model=tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Conv2D(16,kernel_size=(3,3),padding='same',activation=tf.nn.relu,input_shape=(28,28,1)),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=(2,2),strides=(1,1),padding='same'),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Conv2D(32,kernel_size=(3,3),padding='same',activation=tf.nn.relu),\n",
    "    tf.keras.layers.MaxPool2D(pool_size=(2,2),strides=(1,1),padding='same'),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(128,activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Dense(10,activation='softmax')\n",
    "])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d051fba6-9afb-4929-92fd-c76825cf98b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - loss: 0.1803 - sparse_categorical_accuracy: 0.9452 - val_loss: 0.0643 - val_sparse_categorical_accuracy: 0.9805\n",
      "Epoch 2/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 19ms/step - loss: 0.0594 - sparse_categorical_accuracy: 0.9811 - val_loss: 0.0420 - val_sparse_categorical_accuracy: 0.9877\n",
      "Epoch 3/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 19ms/step - loss: 0.0429 - sparse_categorical_accuracy: 0.9870 - val_loss: 0.0360 - val_sparse_categorical_accuracy: 0.9890\n",
      "Epoch 4/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 19ms/step - loss: 0.0325 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.0369 - val_sparse_categorical_accuracy: 0.9881\n",
      "Epoch 5/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 19ms/step - loss: 0.0271 - sparse_categorical_accuracy: 0.9911 - val_loss: 0.0418 - val_sparse_categorical_accuracy: 0.9873\n",
      "Epoch 6/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 19ms/step - loss: 0.0241 - sparse_categorical_accuracy: 0.9925 - val_loss: 0.0400 - val_sparse_categorical_accuracy: 0.9891\n",
      "Epoch 7/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - loss: 0.0193 - sparse_categorical_accuracy: 0.9938 - val_loss: 0.0367 - val_sparse_categorical_accuracy: 0.9898\n",
      "Epoch 8/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - loss: 0.0165 - sparse_categorical_accuracy: 0.9945 - val_loss: 0.0525 - val_sparse_categorical_accuracy: 0.9881\n",
      "Epoch 9/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 19ms/step - loss: 0.0158 - sparse_categorical_accuracy: 0.9942 - val_loss: 0.0367 - val_sparse_categorical_accuracy: 0.9905\n",
      "Epoch 10/10\n",
      "\u001b[1m750/750\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 19ms/step - loss: 0.0149 - sparse_categorical_accuracy: 0.9949 - val_loss: 0.0409 - val_sparse_categorical_accuracy: 0.9901\n",
      "157/157 - 0s - 3ms/step - loss: 0.0366 - sparse_categorical_accuracy: 0.9905\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.036572422832250595, 0.9904999732971191]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#p115\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n",
    "              metrics=['sparse_categorical_accuracy'])\n",
    "history=model.fit(x_train,y_train,batch_size=64,epochs=10,validation_split=0.2)\n",
    "model.evaluate(x_test,y_test,batch_size=64,verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d20ba24c-87a1-4c13-ab57-212a1e49b6e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P116\n",
    "loss=history.history['loss']\n",
    "acc=history.history['sparse_categorical_accuracy']\n",
    "val_loss=history.history['val_loss']\n",
    "val_acc=history.history['val_sparse_categorical_accuracy']\n",
    "plt.figure(figsize=(10,3))\n",
    "plt.subplot(121)\n",
    "plt.plot(loss,color='b',label='train')\n",
    "plt.plot(val_loss,color='r',label='test')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.subplot(122)\n",
    "plt.plot(acc,color='b',label='train')\n",
    "plt.plot(val_acc,color='r',label='test')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa86f55e-2316-40cc-b889-7523a1c39f52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#118\n",
    "plt.figure()\n",
    "for i in range(10):\n",
    "    n=np.random.randint(1,10000)\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plt.axis('off')\n",
    "    plt.rcParams['font.sans-serif']=['Simhei']\n",
    "    plt.imshow(x_test[n],cmap='gray')\n",
    "    demo=tf.reshape(x_test[n],(1,28,28))\n",
    "    y_pred=np.argmax(model.predict(demo),axis=1)\n",
    "    title='标签值:'+str((y_test.numpy())[n])+'\\n'+str(y_pred[0])\n",
    "    plt.title(title)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5793375-9c64-425a-b5fa-49e51927d12b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
